From Image to Imuge: Immunized Image Generation

• URL: http://arxiv.org/abs/2110.14196v1
• Date: Wed, 27 Oct 2021 05:56:15 GMT
• Title: From Image to Imuge: Immunized Image Generation
• Authors: Qichao Ying, Zhenxing Qian, Hang Zhou, Haisheng Xu, Xinpeng Zhang and Siyi Li
• Abstract summary: Imuge is an image tamper resilient generative scheme for image self-recovery. We jointly train a U-Net backboned encoder, a tamper localization network and a decoder for image recovery. We demonstrate that our method can recover the details of the tampered regions with a high quality despite the presence of various kinds of attacks.
• Score: 23.430377385327308
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Imuge, an image tamper resilient generative scheme for image self-recovery. The traditional manner of concealing image content within the image are inflexible and fragile to diverse digital attack, i.e. image cropping and JPEG compression. To address this issue, we jointly train a U-Net backboned encoder, a tamper localization network and a decoder for image recovery. Given an original image, the encoder produces a visually indistinguishable immunized image. At the recipient's side, the verifying network localizes the malicious modifications, and the original content can be approximately recovered by the decoder, despite the presence of the attacks. Several strategies are proposed to boost the training efficiency. We demonstrate that our method can recover the details of the tampered regions with a high quality despite the presence of various kinds of attacks. Comprehensive ablation studies are conducted to validate our network designs.

Related papers

• Transferable Learned Image Compression-Resistant Adversarial Perturbations [66.46470251521947]
  Adversarial attacks can readily disrupt the image classification system, revealing the vulnerability of DNN-based recognition tasks. We introduce a new pipeline that targets image classification models that utilize learned image compressors as pre-processing modules.
  arXiv  Detail & Related papers  (2024-01-06T03:03:28Z)
• IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI [52.90082445349903]
  Diffusion-based image generation models can create artistic images that mimic the style of an artist or maliciously edit the original images for fake content. Several attempts have been made to protect the original images from such unauthorized data usage by adding imperceptible perturbations. In this work, we introduce a purification perturbation platform, named IMPRESS, to evaluate the effectiveness of imperceptible perturbations as a protective measure.
  arXiv  Detail & Related papers  (2023-10-30T03:33:41Z)
• Human-imperceptible, Machine-recognizable Images [76.01951148048603]
  A major conflict is exposed relating to software engineers between better developing AI systems and distancing from the sensitive training data. This paper proposes an efficient privacy-preserving learning paradigm, where images are encrypted to become human-imperceptible, machine-recognizable'' We show that the proposed paradigm can ensure the encrypted images have become human-imperceptible while preserving machine-recognizable information.
  arXiv  Detail & Related papers  (2023-06-06T13:41:37Z)
• Learning to Immunize Images for Tamper Localization and Self-Recovery [30.185576617722713]
  Image immunization (Imuge) is a technology of protecting the images by introducing trivial perturbation. This paper presents Imuge+, an enhanced scheme for image immunization.
  arXiv  Detail & Related papers  (2022-10-28T05:16:56Z)
• StyleGAN Encoder-Based Attack for Block Scrambled Face Images [14.505867475659276]
  We propose an attack method to block scrambled face images, particularly Encryption-then-Compression (EtC) applied images. Instead of reconstructing identical images as plain ones from encrypted images, we focus on recovering styles that can reveal identifiable information from the encrypted images. While state-of-the-art attack methods cannot recover any perceptual information from EtC images, the proposed method discloses personally identifiable information such as hair color, skin color, eyeglasses, gender, etc.
  arXiv  Detail & Related papers  (2022-09-16T14:12:39Z)
• Robust Image Protection Countering Cropping Manipulation [30.185576617722713]
  This paper presents a novel robust watermarking scheme for image Cropping localization and Recovery (CLR-Net) We first protect the original image by introducing imperceptible perturbations. Then, typical image post-processing attacks are simulated to erode the protected image. On the recipient's side, we predict the cropping mask and recover the original image.
  arXiv  Detail & Related papers  (2022-06-06T07:26:29Z)
• Hiding Images into Images with Real-world Robustness [21.328984859163956]
  We introduce a generative network based method for hiding images into images while assuring high-quality extraction. An embedding network is sequentially decoupling with an attack layer, a decoupling network and an image extraction network. We are the first to robustly hide three secret images.
  arXiv  Detail & Related papers  (2021-10-12T02:20:34Z)
• Image Transformation Network for Privacy-Preserving Deep Neural Networks and Its Security Evaluation [17.134566958534634]
  We propose a transformation network for generating visually-protected images for privacy-preserving DNNs. The proposed network enables us not only to strongly protect visual information but also to maintain the image classification accuracy that using plain images achieves.
  arXiv  Detail & Related papers  (2020-08-07T12:58:45Z)
• Modeling Lost Information in Lossy Image Compression [72.69327382643549]
  Lossy image compression is one of the most commonly used operators for digital images. We propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem.
  arXiv  Detail & Related papers  (2020-06-22T04:04:56Z)
• In-Domain GAN Inversion for Real Image Editing [56.924323432048304]
  A common practice of feeding a real image to a trained GAN generator is to invert it back to a latent code. Existing inversion methods typically focus on reconstructing the target image by pixel values yet fail to land the inverted code in the semantic domain of the original latent space. We propose an in-domain GAN inversion approach, which faithfully reconstructs the input image and ensures the inverted code to be semantically meaningful for editing.
  arXiv  Detail & Related papers  (2020-03-31T18:20:18Z)
• Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation [181.08127307338654]
  This work presents an effective way to exploit the image prior captured by a generative adversarial network (GAN) trained on large-scale natural images. The deep generative prior (DGP) provides compelling results to restore missing semantics, e.g., color, patch, resolution, of various degraded images.
  arXiv  Detail & Related papers  (2020-03-30T17:45:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.